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make it heavy

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Basic Information

Make It Heavy is a Python framework designed to emulate Grok heavy-style deep analysis by orchestrating multiple intelligent agents. It uses OpenRouter-compatible LLMs to run either a single tool-enabled agent or a multi-agent workflow that generates specialized research questions, runs parallel analyses, and synthesizes a unified answer. The project provides CLI entry points for single-agent and multi-agent modes, a configurable orchestrator, and an auto-discovered tool system that loads tools from a tools/ directory. It targets developers and researchers who want to run multi-perspective AI analysis, customize behavior via config.yaml, and extend capabilities by adding new tools. Prerequisites and runtime details in the README include Python 3.8+ and an OpenRouter API key.

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App URL
https://github.com/Doriandarko/make-it-heavy
Github Repository
https://github.com/Doriandarko/make-it-heavy/blob/main/README.md

App Details

Features
The repository documents several concrete features implemented in code and config. It supports Grok heavy emulation via a 4-agent parallel orchestration by default and can adjust the number of agents. It performs dynamic question generation to create four specialized prompts, provides live progress and visual feedback during execution, and includes a synthesis agent that merges multiple perspectives. Tools are auto-discovered and hot-swappable from the tools/ directory and inherit from BaseTool. Provided tools include web search, safe calculation, file read/write, and task completion signaling. Configuration and model selection are controlled in config.yaml, with support for multiple OpenRouter-compatible models and explicit orchestration and timeout settings.
Use Cases
Make It Heavy helps developers and analysts obtain multi-perspective, synthesized answers for complex queries by automating question generation, parallel analysis, and response synthesis. It reduces manual orchestration by handling agent lifecycle, tool integration, error handling, and timeouts, and it exposes CLI entry points for running single-agent tasks or the multi-agent Grok heavy flow. The tool system makes it easy to extend functionality by adding new BaseTool implementations, and config options let users change model choices, parallelism, and timeouts. Example use cases in the README include research queries, technical troubleshooting, and structured creative tasks where combined agent viewpoints produce more comprehensive outputs than a single model run.

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